Handwritten Arabic Documents Indexation using HOG Feature

Elfakir, Youssef; Khaissidi, Ghizlane; Mrabti, Mostafa; Chenouni, Driss

doi:10.5120/ijca2015906182

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Applying Support Vector Machine on features extracted by CNN [35] achieved 92.95% recognition rate. This CNN-based features outperformed other SVM approaches when applied on other traditional features [7], [20], [38]. Accordingly, our approach studied the effect of SVM and CNN in the classification phase.…”

Section: G Conclusion and Resultsmentioning

confidence: 92%

“…Input strings were represented by pyramidal histograms of characters (PHOC) [36] to be encoded at different levels. Also, pyramid histogram of oriented gradients (PHoG) [37] outperformed the ordinary histograms of gradients (HoG) [7], [20], [38]. Based on literature [5], [39], derivatives were also computed in recently proposed systems.…”

Section: Features Extractionmentioning

confidence: 99%

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Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be documented digitally. If we compared between Latin and the isolated Arabic character recognition, the latter is much more challenging due to the similarity between characters, and the variability of the writing styles. This paper proposes a multistage cascading system to serve the field of offline Arabic handwriting recognition. The approach starts with applying the Hierarchical Agglomerative Clustering (HAC) technique to split the database into partially interrelated clusters. The interrelations between the constructed clusters support representing the database as a big search tree model and help to attain a reduced complexity in matching each test image with a cluster. Cluster members are then ranked based on our new proposed ranking algorithm. This ranking algorithm starts with computing Pyramid Histogram of Oriented Gradients (PHoG), and is followed by measuring divergence by Kullback-Leibler method. Eventually, the classification process is applied only to the highly ranked matching classes. A comparative study is made to assess the effect of six different deep Convolution Neural Networks (DCNNs) on the final recognition rates of the proposed system. Experiments are done using the IFN/ENIT Arabic database. The proposed clustering and ranking stages lead to using only 11% of the whole database in classifying test images. Accordingly, more reduced computation complexity and more enhanced classification results are achieved compared to recent existing systems. INDEX TERMS Agglomerative hierarchical clustering, deep convolutional neural network, Kullback-Leibler divergence, offline arabic handwritting recognition, pyramid histogram of gradients.

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Section: G Conclusion and Resultsmentioning

confidence: 92%

Section: Features Extractionmentioning

confidence: 99%

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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“…-Cursivity of the Arabic script -Arabic language contains many diacritic marks -Form of the same letter at the beginning and end of a word can be change -People write with their own script The word spotting process needs enough time and effort to be performed by manual inspection. To facilitate the search in numerical document images, numerous word spotting researchers had based on text line or word segmentation steps [3][4][5][6]. First, an initial step is performed to segment text into word candidates [7].…”

Section: Introductionmentioning

confidence: 99%

The impact of the image processing in the indexation system

Elfakir

Khaissidi

Mrabti

et al. 2019

IJECE

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This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system.

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“…Jon et al [8] represent the document with a grid of HOG descriptors and use a sliding-window approach for word spotting in document images. A similar approach is used in [9] which the retrieval step is performed by using an exemplar support vector machine framework.…”

Section: Introductionmentioning

confidence: 99%

Segmentation-free Word Spotting for Handwritten Arabic Documents

Khaissidi¹,

Elfakir²,

Mrabti³

et al. 2016

IJIMAI

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-In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM).

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Handwritten Arabic Documents Indexation using HOG Feature

Cited by 5 publications

References 11 publications

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

The impact of the image processing in the indexation system

Segmentation-free Word Spotting for Handwritten Arabic Documents

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